Adaptive Subspace Signal Detection and Performance Analysis in Nonzero-Mean Clutter
Weijian Liu; Zhenyu Xu; Jun Liu; Hui Chen; Yongxiang Liu · 2026 · IEEE Transactions on Signal Processing
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
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Abstract
To solve the problem of detecting subspace signals in nonzero-mean clutter, we propose adaptive detectors, based on the strategies of generalized likelihood ratio test (GLRT), Rao test, Wald test, gradient test, and Durbin test. The results show that the detectors based on GLRT, Rao and Wald are structurally consistent with the subspace detectors in zero-means clutter. The analytic expressions for the probability of detection (PD) and probability of false alarm (PFA) of each detector are derived, and two major performance differences in the nonzero-mean clutter scenario are revealed. One is th
Abstract by Weijian Liu; Zhenyu Xu; Jun Liu; Hui Chen; Yongxiang Liu, IEEE Transactions on Signal Processing (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1109/tsp.2026.3692130